:: Library Catalog

Imagen de Portada

Guardado en:

Detalles Bibliográficos
Autores principales:	Pereira, Jansen S. B., Valdrighi, Giovani, Raimundo, Marcos Medeiros
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning
Acceso en línea:	https://arxiv.org/abs/2504.12458
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Long-term Fairness with Selective Labels
por: Valdrighi, Giovani, et al.
Publicado: (2026)

Best Practices for Responsible Machine Learning in Credit Scoring
por: Valdrighi, Giovani, et al.
Publicado: (2024)

Bandit Max-Min Fair Allocation
por: Harada, Tsubasa, et al.
Publicado: (2025)

Balancing Performance and Reject Inclusion: A Novel Confident Inlier Extrapolation Framework for Credit Scoring
por: Ribeiro, Athyrson Machado, et al.
Publicado: (2025)

MinMaxMin $Q$-learning
por: Soffair, Nitsan, et al.
Publicado: (2024)

The Limit Points of (Optimistic) Gradient Descent in Min-Max Optimization
por: Daskalakis, Constantinos, et al.
Publicado: (2018)

Bridging Jensen Gap for Max-Min Group Fairness Optimization in Recommendation
por: Xu, Chen, et al.
Publicado: (2025)

Bayesian Neural Networks: A Min-Max Game Framework
por: Hong, Junping, et al.
Publicado: (2023)

Transformer-Based Power Optimization for Max-Min Fairness in Cell-Free Massive MIMO
por: Chafaa, Irched, et al.
Publicado: (2025)

Multi-criteria Rank-based Aggregation for Explainable AI
por: Chatterjee, Sujoy, et al.
Publicado: (2025)

Smooth Min-Max Monotonic Networks
por: Igel, Christian
Publicado: (2023)

Retain-Neutral Surrogates for Min-Max Unlearning
por: Cai, Junhao, et al.
Publicado: (2026)

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling
por: Zhao, Xiyu, et al.
Publicado: (2025)

Near-Optimal Privacy-Preserving Learning for Max-Min Fair Multi-Agent Bandits
por: Leshem, Amir
Publicado: (2023)

A Single-Loop Smoothed Gradient Descent-Ascent Algorithm for Nonconvex-Concave Min-Max Problems
por: Zhang, Jiawei, et al.
Publicado: (2020)

A Novel Gaussian Min-Max Theorem and its Applications
por: Akhtiamov, Danil, et al.
Publicado: (2024)

Diffusion Stochastic Optimization for Min-Max Problems
por: Cai, Haoyuan, et al.
Publicado: (2024)

Min-p, Max Exaggeration: A Critical Analysis of Min-p Sampling in Language Models
por: Schaeffer, Rylan, et al.
Publicado: (2025)

Robust Variational Bayes by Min-Max Median Aggregation
por: Yan, Jiawei, et al.
Publicado: (2025)

Granularity at Scale: Estimating Neighborhood Socioeconomic Indicators from High-Resolution Orthographic Imagery and Hybrid Learning
por: Brewer, Ethan, et al.
Publicado: (2023)

MinMax Recurrent Neural Cascades
por: Ronca, Alessandro
Publicado: (2026)

Statistical Mechanics of Min-Max Problems
por: Ichikawa, Yuma, et al.
Publicado: (2024)

Collaborative Min-Max Regret in Grouped Multi-Armed Bandits
por: Blanchard, Moïse, et al.
Publicado: (2025)

Constrained Multi-Objective Reinforcement Learning with Max-Min Criterion
por: Park, Giseung, et al.
Publicado: (2026)

Implicit Riemannian Optimism with Applications to Min-Max Problems
por: Roux, Christophe, et al.
Publicado: (2025)

Doubly-Regressing Approach for Subgroup Fairness
por: Kim, Kunwoong, et al.
Publicado: (2025)

On the Convergence of Min-Max Langevin Dynamics and Algorithm
por: Cai, Yang, et al.
Publicado: (2024)

Reasoning Distillation and Structural Alignment for Improved Code Generation
por: Jalilifard, Amir, et al.
Publicado: (2025)

Revisiting Min-Max Optimization Problem in Adversarial Training
por: Ahmadi, Sina Hajer, et al.
Publicado: (2024)

A Biased Estimator for MinMax Sampling and Distributed Aggregation
por: Wolfrath, Joel, et al.
Publicado: (2024)

MaxMin-RLHF: Alignment with Diverse Human Preferences
por: Chakraborty, Souradip, et al.
Publicado: (2024)

Bayesian Optimization for Function-Valued Responses under Min-Max Criteria
por: Ahadi, Pouya, et al.
Publicado: (2025)

Accelerated Stochastic Min-Max Optimization Based on Bias-corrected Momentum
por: Cai, Haoyuan, et al.
Publicado: (2024)

Decentralized Personalized Federated Learning for Min-Max Problems
por: Borodich, Ekaterina, et al.
Publicado: (2021)

Second-Order Min-Max Optimization with Lazy Hessians
por: Chen, Lesi, et al.
Publicado: (2024)

Distribution-Specific Auditing For Subgroup Fairness
por: Hsu, Daniel, et al.
Publicado: (2024)

FairTree: Subgroup Fairness Auditing of Machine Learning Models with Bias-Variance Decomposition
por: Debelak, Rudolf
Publicado: (2026)

Min-Max Optimization Is Strictly Easier Than Variational Inequalities
por: Shugart, Henry, et al.
Publicado: (2025)

Multi-Objective Reinforcement Learning with Max-Min Criterion: A Game-Theoretic Approach
por: Byeon, Woohyeon, et al.
Publicado: (2025)

Investigating Data Interventions for Subgroup Fairness: An ICU Case Study
por: Tan, Erin, et al.
Publicado: (2026)